The Pharmacovigilance Guidelines for Clinical Application of Oral Chinese Patent Medicines （hereinafter referred to as the Guidelines） is first specialized in the field of drug safety for oral Chinese patent medicines （OCPMs） in China. Rooted in China's healthcare context， the Guidelines address the unique usage patterns and risk characteristics of OCPMs， filling a regulatory gap in the pharmacovigilance framework specific to this category. To facilitate accurate understanding and effective implementation of the Guidelines， and to promote the standardized development of pharmacovigilance practices for OCPMs， this study offered a systematic interpretation based on its three core components. In the domain of risk monitoring and reporting， the paper analyzed the rationale for multi-source information integration and clarified the criteria for identifying key products and target populations for intensive monitoring. Regarding risk assessment， the Guidelines were examined from three dimensions of formulation components， medication behaviors， and population to address complex safety issues arising from medicinal constituents， irrational use， and individual susceptibility. In the area of risk control， the analysis focused on context-based interventions and dynamic closed-loop management strategies， exploring practical pathways to shift from passive response to proactive risk mitigation. Furthermore， this paper evaluated the applied value of the Guidelines and identified implementation challenges， such as insufficient capacity at the primary-care level and limited digital infrastructure. In response， the study proposed optimization strategies including establishing a dynamic updating mechanism， strengthening training at the grassroots level， and incorporating artificial intelligence to enhance pharmacovigilance capacity. This interpretation aims to provide actionable insights for marketing authorization holders （including manufacturers）， pharmaceutical distributors， healthcare institutions， and research organizations， ultimately supporting the establishment and refinement of a full lifecycle pharmacovigilance system for OCPMs.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To analyze the influencing factors for postoperative anastomotic leak (AL) in carcinoma of the esophagus and gastroesophageal junction and construct a nomogram predictive model. Methods The patients who underwent radical esophagectomy at Jinling Hospital Affiliated to Nanjing University School of Medicine from January 2018 to June 2020 were included in this study. Relevant variables were screened using univariate and multivariate logistic regression analyses. A nomogram was then developed to predict the risk factors associated with postoperative AL. The predictive performance of the nomogram was validated using the receiver operating characteristic (ROC) curve. Results A total of 468 patients with carcinoma of the esophagus and gastroesophageal junction were included in the study, comprising 354 males and 114 females, with a mean age of (62.8±7.2) years. The tumors were predominantly located in the middle or lower esophagus, and 51 (10.90%) patients experienced postoperative AL. Univariate logistic regression analysis indicated that age, body mass index (BMI), tumor location, preoperative albumin levels, diabetes mellitus, anastomosis technique, anastomosis site, and C-reactive protein (CRP) levels were potentially associated with AL (P<0.05). Multivariate logistic regression analysis identified age, BMI, tumor location, diabetes mellitus, anastomosis technique, and CRP levels as independent risk factors for AL (P<0.05). A nomogram was developed based on the findings from the multivariate logistic regression analysis. The area under the receiver operating characteristic (ROC) curve was 0.803, indicating a strong concordance between the actual observations and the predicted outcomes. Furthermore, decision curve analysis demonstrated that the newly established nomogram holds significant value for clinical decision-making. Conclusion The predictive model for postoperative AL in patients with carcinoma of the esophagus and gastroesophageal junction demonstrates strong predictive validity and is essential for guiding clinical monitoring, early detection, and preventive strategies.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Background/Aims: Distal mean nocturnal baseline impedance (MNBI) measuring via pH-impedance may be valuable in diagnosing patients with suspected laryngopharyngeal reflux (LPR). However, its wide adoption is hindered by cost and invasiveness. This study investigates whether baseline impedance measured during high-resolution impedance manometry (HRIM-BI) can predict pathological MNBI. Methods: A cross-sectional study in Taiwan included 74 subjects suspected of LPR, who underwent HRIM (MMS) and pH-impedance testing (Diversatek), after stopping proton pump inhibitors for more than 7 days. Subjects with grade C or D esophagitis or Barrett’s esophagus were excluded. The cohort was divided into 2 groups: those with concomitant typical reflux symptoms (CTRS, n = 28) and those with isolated LPR symptoms (ILPRS, n = 46). HRIM-BI measurements focused on both distal and proximal esophagi. Pathological MNBI was identified as values below 2065 Ω, measured 3 cm above the lower esophageal sphincter. Results: In all subjects, distal HRIM-BI values correlated weakly with distal MNBI(r = 0.34-0.39, P < 0.005). However, in patients with ILPRS, distal HRIM-BI corelated moderately with distal MNBI(r = 0.43-0.48, P < 0.005). The areas under the receiver operating characteristic curve was 0.78 (P = 0.001) with a sensitivity of 0.83 and a specificity of 0.68. No correlation exists between distal HRIM-BI and distal MNBI in patients with CTRS, and between proximal HRIM-BI and proximal MNBI in both groups. Conclusions Distal HRIM-BI from HRIM may potentially predict pathological MNBI in patients with ILPRS, but not in those with CTRS. Future outcome studies linked to the metric are warranted.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Background/Aims: Distal mean nocturnal baseline impedance (MNBI) measuring via pH-impedance may be valuable in diagnosing patients with suspected laryngopharyngeal reflux (LPR). However, its wide adoption is hindered by cost and invasiveness. This study investigates whether baseline impedance measured during high-resolution impedance manometry (HRIM-BI) can predict pathological MNBI. Methods: A cross-sectional study in Taiwan included 74 subjects suspected of LPR, who underwent HRIM (MMS) and pH-impedance testing (Diversatek), after stopping proton pump inhibitors for more than 7 days. Subjects with grade C or D esophagitis or Barrett’s esophagus were excluded. The cohort was divided into 2 groups: those with concomitant typical reflux symptoms (CTRS, n = 28) and those with isolated LPR symptoms (ILPRS, n = 46). HRIM-BI measurements focused on both distal and proximal esophagi. Pathological MNBI was identified as values below 2065 Ω, measured 3 cm above the lower esophageal sphincter. Results: In all subjects, distal HRIM-BI values correlated weakly with distal MNBI(r = 0.34-0.39, P < 0.005). However, in patients with ILPRS, distal HRIM-BI corelated moderately with distal MNBI(r = 0.43-0.48, P < 0.005). The areas under the receiver operating characteristic curve was 0.78 (P = 0.001) with a sensitivity of 0.83 and a specificity of 0.68. No correlation exists between distal HRIM-BI and distal MNBI in patients with CTRS, and between proximal HRIM-BI and proximal MNBI in both groups. Conclusions Distal HRIM-BI from HRIM may potentially predict pathological MNBI in patients with ILPRS, but not in those with CTRS. Future outcome studies linked to the metric are warranted.

OBJECTIVE: To investigate the effect of miR-483-5p on hepatocellular carcinoma (HCC) cells proliferation and stemness, as well as the attenuating effect of Pien Tze Huang (PZH). METHODS: Differentially expressed miRNA between HepG2 cells and hepatic cancer stem-like cells (HCSCs) were identified by a miRNA microarray assay. miR-483-5p mimics were transfected into HepG2 cells to explore the effects of miR-483-5p on cell proliferation and stemness. HepG2 cells and HCSCs were treated with PZH (0, 0.25, 0.50 and 0.75 mg/mL) to explore the effects of PZH on the proliferation and stemness, both in non-induced state and the state induced by miR-483-5p mimics. RESULTS: miR-483-5p was significantly up-regulated in HCSCs and its overexpression increased cell proliferation and stemness in HepG2 cells (P<0.05). PZH not only significantly inhibited proliferation in HepG2 cells, but also significantly suppressed the cell proliferation and self-renewal of HCSCs (P<0.05). The effects of miR-483-5p mimics on proliferation and stemness of HepG2 cells were partially abolished by PZH. CONCLUSIONS miR-483-5p promotes proliferation and enhances stemness of HepG2 cells, which were attenuated by PZH, demonstrating that miR-483-5p is a potential molecular target for the treatment of HCC and provide experimental evidence to support clinical use of PZH for patients with HCC.
Humans ; MicroRNAs/metabolism* ; Cell Proliferation/drug effects* ; Liver Neoplasms/drug therapy* ; Carcinoma, Hepatocellular/drug therapy* ; Hep G2 Cells ; Neoplastic Stem Cells/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Gene Expression Regulation, Neoplastic/drug effects*